SAR image de-noising via grouping-based PCA and guided filter
نویسندگان
چکیده
منابع مشابه
SAR Image De-Noising Based on Shift Invariant K-SVD and Guided Filter
Finding a way to effectively suppress speckle in SAR images has great significance. K-means singular value decomposition (K-SVD) has shown great potential in SAR image de-noising. However, the traditional K-SVD is sensitive to the position and phase of the characteristics in the image, and the de-noised image by K-SVD has lost some detailed information of the original image. In this paper, we p...
متن کاملHistoric Document Image De-noising Using Principal Component Analysis (PCA) and Local Pixel Grouping (LPG)
In this paper, an approach of principal component analysis (PCA) with local pixel grouping (LPG) is used to de-noising the noisy historical document image. This technique ensures the preservation of historic document image local structure. This is due to block matching based LPG which carries out classification to allow only the sample blocks with similar contents used in the calculation for PC...
متن کاملImage De-Noising and Micro Crack Detection of Solar Cells
Solar cell is known as a sustainable and environment friendly source of energy in nature. It converts sunlight directly into electricity with zero emission and also without side-effects on the environment. But, solar cells have optical and mechanical defects which include the type of micro crack, the size of crack, and the noise from electrical or electromechanical interference during the image...
متن کاملA Fast and Robust Hybridized Filter for Image De-Noising
In this research, we will work on the development of a new method for the removal of salt & pepper noise by creating a new hybridized filter using existing and/or new noise removal filters. The proposed filter will remove the noise with no or minimum image quality degradation. Salt & pepper noise degrades the quality of the image by hiding the details of objects in the image and also causes dam...
متن کاملDe-noising and Recovering Images Based on Kernel PCA Theory
ABSTRACT Principal Component Analysis (PCA) is a basis transformation to diagonalize an estimate of the covariance matrix of input data and, the new coordinates in the Eigenvector basis are called principal components. Since Kernel PCA is just a PCA in feature space F , the projection of an image in input space can be reconstructed from its principal components in feature space. This enables us...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Systems Engineering and Electronics
سال: 2021
ISSN: 1004-4132
DOI: 10.23919/jsee.2021.000009